Predicting user navigation events

ABSTRACT

A method and system for predicting a next navigation event are described. Aspects of the disclosure minimize the delay between a navigation event and a network response by predicting the next navigation event. The system and method may then prerender content associated with the next navigation event. For example, the method and system may predict a likely next uniform resource locator during web browsing to preemptively request content from the network before the user selects the corresponding link on a web page. The methods describe a variety of manners of predicting the next navigation event, including examining individual and aggregate historical data, text entry prediction, and cursor input monitoring.

BACKGROUND

The advent of the World Wide Web has placed more information at thefingertips of today's users than ever before. Various websites cater tonearly every need and interest, providing access to referenceinformation, business and financial documents, social networking, andmore. Widespread broadband Internet access provides faster access tothese sites than ever before.

However, as fast as current high-speed Internet services are, the act ofbrowsing the web is not instantaneous. When a user selects a link on apage or enters a uniform resource locator (URL) in a text field, thereis a delay while data is requested from the host, sent to the client,and rendered in the browser. The user is typically idle while waitingfor their requested site to load. While high-speed Internet access maylimit this delay to a few seconds, even this short delay can add up tothousands of man-hours of lost productivity each year.

BRIEF SUMMARY

A method and system for predicting user navigation events are described.Aspects of the disclosure minimize the delay in accessing web content bypredicting a user navigation event on a web page. The navigation eventmay be predicted by various indicators, including but not limited to auser's navigation history, aggregate navigation history, text entrywithin a data entry field, or a mouse cursor position. Users can beprovided with an opportunity to op in/out of functionality that maycollect personal information about users. In addition, certain data canbe anonymized and aggregated before it is stored or used, such thatpersonally identifiable information is removed.

In one aspect, the disclosure describes a computer-implemented methodfor predicting a navigation event. The method comprises receiving anindicator of navigational intent, predicting, using a processor, a nextnavigation event from the indicator, and prerendering content associatedwith the next navigation event. The next navigation event is a uniformresource locator, and the indicator is at least one of a browsinghistory, a text entry, or a cursor input.

In another aspect, the disclosure describes a computer-implementedmethod for predicting a navigation event. The method comprises trackinga navigation history calculating one or more confidence values for oneor more of a plurality of navigation events using the navigationhistory, determining, using a processor, one or more likely navigationevents using the confidence values, and identifying at least one of theone or more likely navigation events as a predicted navigation event.The method may further include retrieving content associated with thepredicted navigation event. In some aspects, the stage of calculatingthe one or more confidence values comprises monitoring for the selectionof a first uniform resource locator, incrementing, in response to theselection, a frequency value associated with the first uniform resourcelocator or a frequency value associated with a pair of uniform resourcelocators, storing the frequency value in a memory, and determining aconfidence value for the uniform resource locator or pair of uniformresource locators from at least one frequency value stored in thememory. The pair of uniform resource locators may comprise the firstuniform resource locator and a source uniform resource locator.

In another aspect, the method comprises decaying a frequency value fornon-selected uniform resource locators after a predetermined timeinterval. The decaying of the frequency value for the non-selecteduniform resource locators may be performed in response to the selectionof the first uniform resource locator. In yet another aspect, thenavigation history is associated with at least one of a particularclient or a particular user. In a yet further aspect, the navigationhistory is associated with a plurality of users.

In another aspect, the method may further comprise computing a firsthash value for a navigation event associated with a first uniformresource locator or a transitional pair of uniform resource locators,computing a confidence value for the navigation event, and transmittingthe hash value and the confidence value, such that a receiver of thefirst hash value and the confidence value computes a second hash valueof a second uniform resource locator to identify the first uniformresource locator to which the confidence value applies. The transitionalpair may comprise a source uniform resource locator and a destinationuniform resource locator. In some aspects of the method, the stage ofdetermining the most likely navigation event comprises computing, for atleast one uniform resource locator (URL), a most visited subsequent URLbased on the navigation history of the plurality of users. In anotheraspect, the method further comprises determining if the number of visitsto the subsequent uniform resource locator is greater than a thresholdnumber of visits.

In another aspect, the method may further comprise determining if anumber of users submitting data for the subsequent uniform resourcelocator is greater than a threshold number of users. Another aspect ofthe method further includes identifying a window of recent visits to beanalyzed to determine the most visited subsequent URL, and analyzingvisits within the identified window. In some aspects, the window isspecified by a time period or a number of visits.

In further aspects of the method, the navigation history comprises atleast one of a uniform resource locator or a transitional pair ofuniform resource locators. The transitional pair of uniform resourcelocators comprises a source uniform resource locator and a destinationuniform resource locator.

In further aspects, the disclosure describes a method of predicting anext navigation event. The method comprises receiving a set of data fora uniform resource locator, computing, using a processor, a hash valuefor one or more links present on a page associated with the uniformresource locator, comparing the computed hash values with the receivedhash values to map each computed hash value to a received hash value,and identifying a confidence value associated with each visible linkbased upon the received confidence value associated with the receivedhash value to which the computed hash value for the link maps. The setof data comprises hash values associated with one or more linksassociated with the uniform resource locator and a set of confidencevalues associated with the one or more links. In another aspect, themethod further comprises predicting one or more next navigation events,where the one or more predicted next navigation events relate to a linkwith the highest identified confidence value.

Yet further aspects of the disclosure describe a method for predicting anext navigation event. The method includes monitoring text entry withina text entry field, predicting, using a processor, a likely uniformresource locator or likely query based upon the text entry, andidentifying the likely uniform resource locator or likely query as apredicted next navigation event. In some aspects, predicting the likelyURL includes comparing the text entry with a user history to identify apreviously visited uniform resource locator. Predicting the likely querymay also comprise comparing the text entry with a set of previouslyentered search queries to identify a likely next query as the nextnavigation event. In some aspects, the method further includesidentifying a set of search results associated with the identifiedlikely next query. In yet further aspects, the method may includeidentifying a most relevant search result from the set of search resultsas the predicted next navigation event. The stage of predicting thelikely query may include receiving a set of possible queries from asearch engine based upon the text entry.

In further aspects, the disclosure may comprise a computer-implementedmethod for predicting a next navigation event. The method comprisesmonitoring movement of a cursor within a browser, and predicting, usinga processor, a next navigation event by identifying at least one of ahyperlink toward which the cursor is moving or a hyperlink on which thecursor is located. The browser displays a web page with one or morehyperlinks. Additional aspects of the method further includeprerendering a web page associated with the identified hyperlink.Aspects of the method may further include extrapolating the movement ofthe cursor to identify a line, and identifying one or more of thehyperlinks on the identified line as the next navigation event. In someaspects, the method further includes calculating a speed of the cursorand a distance to each of the hyperlinks to determine to which of thehyperlinks the cursor is likely to be traveling.

In another aspect, the disclosure provides a processing system forpredicting a next navigation event. The processing system comprises atleast one processor, a navigation prediction module associated with theat least one processor, and memory for storing navigation data. Thememory is coupled to the at least one processor. the navigationprediction module is configured to calculate one or more confidencevalues for one or more of a plurality of navigation events using thenavigation data, to determine one or more likely navigation events usingthe confidence values, and to identify at least one of the one or morelikely navigation events as a predicted navigation event.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a system diagram depicting an example of a server incommunication with example client devices in accordance with aspects ofthe disclosure.

FIG. 2 is block diagram depicting an example computing device inaccordance with aspects of the disclosure.

FIG. 3 is a flow diagram depicting an example method for prerendering aweb page based upon a predicted navigation event in accordance withaspects of the disclosure.

FIG. 4 is a flow diagram depicting an example method for predicting anavigation event based on a client navigation history in accordance withaspects of the disclosure.

FIG. 5 is a flow diagram depicting an example method for computing aconfidence value for a URL using a client navigation history inaccordance with aspects of the disclosure.

FIG. 6 is a flow diagram depicting an example method for predicting anavigation event based on an aggregate navigation history in accordancewith aspects of the disclosure.

FIG. 7 is a flow diagram depicting an example method for computing aconfidence value for a URL using an aggregate navigation history inaccordance with aspects of the disclosure.

FIG. 8A is a flow diagram depicting an example method for predicting anavigation event based on an aggregate navigation history using hashvalues to anonymously manage link data in accordance with aspects of thedisclosure.

FIG. 8B is an illustration of an example web browser employing anexample method for predicting a user navigation event based on anaggregate navigation history in accordance with aspects of thedisclosure.

FIG. 9A is a flow diagram depicting an example method for predicting anavigation event based on data entered within a text field in accordancewith aspects of the disclosure.

FIG. 9B is an illustration of an example web browser employing anexample method for predicting a user navigation event based on dataentered within a text field in accordance with aspects of thedisclosure.

FIG. 10A is a flow diagram depicting an example method for predicting anavigation event based on mouse cursor movement in accordance withaspects of the disclosure.

FIG. 10B is an illustration of an example web browser employing anexample method for predicting a user navigation event based on a mousecursor movement in accordance with aspects of the disclosure.

DETAILED DESCRIPTION

Embodiments of a system and method for predicting user navigation eventsare described herein. Aspects of this disclosure minimize the delaybetween a navigation event and a network response by predicting the nextnavigation event. The system and method may prerender content associatedwith the next navigation event. For example, the method and system maypredict a likely next uniform resource locator during web browsing topreemptively request content from the network before the user selectsthe corresponding link, thus reducing or eliminating the wait time whena user selects a hyperlink on a web page. Various methods describing avariety of manners of predicting the next navigation event, includingexamining individual and aggregate historical data, text entryprediction, and cursor input monitoring are described. Aspects of thedisclosure relate to the prediction of the immediate user navigation(e.g. the next link the user is likely to select when viewing aparticular web page, such as within the next 30 seconds, the nextminute, or the next 5 minutes).

As shown in FIG. 1, an example system 100 in accordance with oneembodiment includes a server 104 in communication (via a network 112)with one or more client devices 106, 108, 110 displaying web browserinterfaces 114, 116, 118.

The client devices 106, 108, 110 are operable to perform prerenderingoperations during the execution of a web browser application. The server104 may transmit navigation history data to the client devices 106, 108,110, to enable prediction of a next navigation event. In some aspects,the client devices 106, 108, 110 determine a next navigation event usinga local navigation history and generate a web request to the server 104to prerender the content associated with the next navigation event. Forexample, the user of the client device 106 may browse to a web pagelocated at “www.fakeaddress.com” as displayed on the web browserinterface 112. That page includes content selectable by the user. Basedon the user's navigation history, the client device 106 may determinewhich of the selectable content the user is likely to select, and thenprerender the content associated with the selectable content byrequesting the content from the server 104.

As another example, the client device 108 may displaywww.fakeaddress.com within a browser 114. The client device 108 mayreceive an aggregate set of navigation statistics from the server 104,and then determine which selectable content the user is likely to selectbased upon the aggregate set of navigation statistics. As yet anotherexample, the client device 110 may display www.fakeaddress.com within abrowser 116. The client device 108 may determine which selectablecontent the user is likely to select based upon a cursor position withinthe browser 114.

While the concepts described herein are generally discussed with respectto a web browser, aspects of the disclosure can be applied to anycomputing node capable of managing navigation events over a network,including a server 104.

The client devices 106, 108, 110 may be any device capable managing datarequests via a network 112. Examples of such client devices include apersonal computer (PC) 108, a mobile device 110, or a server 104. Theclient devices 106, 108, 110 may also comprise personal computers,personal digital assistants (“PDA”): tablet PCs, netbooks, etc. Indeed,client devices in accordance with the systems and methods describedherein may comprise any device operative to process instructions andtransmit data to and from humans and other computers including generalpurpose computers, network computers lacking local storage capability,etc.

The client devices 106, 108, 110 are operable to predict navigationevents to assist in data access on the network 112. For example, theclient devices may predict a likely navigation event to facilitateprerendering of a web page in order to improve the user's browsingexperience. In some aspects, the server 104 provides navigation datathat may be used by the client devices 106, 108, 110 to predict a likelynavigation event (See FIGS. 6-8). In some aspects, the client devices106, 108, 110 predict a likely navigation event using local data. (SeeFIGS. 3-5, 9-10).

The network 112, and the intervening nodes between the server 104 andthe client devices 106, 108, 110, may comprise various configurationsand use various protocols including the Internet, World Wide Web,intranets, virtual private networks, local Ethernet networks, privatenetworks using communication protocols proprietary to one or morecompanies, cellular and wireless networks (e.g., Wi-Fi), instantmessaging, hypertext transfer protocol (“HTTP”) and simple mail transferprotocol (“SMTP”), and various combinations of the foregoing. It shouldbe appreciated that a typical system may include a large number ofconnected computers.

Although certain advantages are obtained when information is transmittedor received as noted above, other aspects of the system and method arenot limited to any particular manner of transmission of information. Forexample, in some aspects, information may be sent via a medium such asan optical disk or portable drive. In other aspects, the information maybe transmitted in a non-electronic format and manually entered into thesystem.

Although some functions are indicated as taking place on the server 104and other functions are indicated as taking place on the client devices106, 108, 110, various aspects of the system and method may beimplemented by a single computer having a single processor. It should beappreciated that aspects of the system and method described with respectto the client may be implemented on the server, and vice-versa.

FIG. 2 is a block diagram depicting an example of a computing device200, such as one of the client devices 106, 108, 110 described withrespect to FIG. 1. The computing device 200 may include a processor 204,a memory 202 and other components typically present in general purposecomputers. The memory 202 may store instructions and data that areaccessible by the processor 204. The processor 204 may execute theinstructions and access the data to control the operations of thecomputing device 200.

The memory 202 may be any type of memory operative to store informationaccessible by the processor 120, including a computer-readable medium,or other medium that stores data that may be read with the aid of anelectronic device, such as a hard-drive, memory card, read-only memory(“ROM”), random access memory (“RAM”), digital versatile disc (“DVD”) orother optical disks, as well as other write-capable and read-onlymemories. The system and method may include different combinations ofthe foregoing, whereby different portions of the instructions and dataare stored on different types of media.

The instructions may be any set of instructions to be executed directly(such as machine code) or indirectly (such as scripts) by the processor204. For example, the instructions may be stored as computer code on acomputer-readable medium. In that regard, the terms “instructions” and“programs” may be used interchangeably herein. The instructions may bestored in object code format for direct processing by the processor 204,or in any other computer language including scripts or collections ofindependent source code modules that are interpreted on demand orcompiled in advance. Functions, methods and routines of the instructionsare explained in more detail below (See FIGS. 3-10).

Data may be retrieved, stored or modified by processor in accordancewith the instructions. For instance, although the architecture is notlimited by any particular data structure, the data may be stored incomputer registers, in a relational database as a table having aplurality of different fields and records, Extensible Markup Language(“XML”) documents or flat files. The data may also be formatted in anycomputer readable format such as, but not limited to, binary values orUnicode. By further way of example only, image data may be stored asbitmaps comprised of grids of pixels that are stored in accordance withformats that are compressed or uncompressed, lossless (e.g., BMP) orlossy (e.g., JPEG), and bitmap or vector-based (e.g., SVG), as well ascomputer instructions for drawing graphics. The data may comprise anyinformation sufficient to identify the relevant information, such asnumbers, descriptive text, proprietary codes, references to data storedin other areas of the same memory or different memories (including othernetwork locations) or information that is used by a function tocalculate the relevant data.

The processor 204 may be any suitable processor, such as variouscommercially available general purpose processors. Alternatively, theprocessor may be a dedicated controller such as an application-specificintegrated circuit (“ASIC”).

Although FIG. 2 functionally illustrates the processor and memory asbeing within a single block, it should be understood that the processor204 and memory 202 may comprise multiple processors and memories thatmay or may not be stored within the same physical housing. Accordingly,references to a processor, computer or memory will be understood toinclude references to a collection of processors, computers or memoriesthat may or may not operate in parallel.

The computing device 200 may be at one node of a network and beoperative to directly and indirectly communicate with other nodes of thenetwork. For example, the computing device 200 may comprise a web serverthat is operative to communicate with client devices via the networksuch that the computing device 200 uses the network to transmit anddisplay information to a user on a display of the client device.

In some examples, the system provides privacy protections for the clientdata including, for example, anonymization of personally identifiableinformation, aggregation of data, filtering of sensitive information,encryption, hashing or filtering of sensitive information to removepersonal attributes, time limitations on storage of information, and/orlimitations on data use or sharing. Data can be anonymized andaggregated such that individual client data is not revealed.

In order to facilitate the navigation event prediction operations of thecomputing device 200, the memory 202 may further comprise a browser 206,a navigation prediction module 208, a prerender module 210, a clientnavigation history 212, and an aggregate navigation history 214.Although a number of discrete modules (e.g., 206, 208, 210, 212 and 214)are identified in connection with FIG. 2, the functionality of thesemodules can overlap and/or exist in a fewer or greater number of modulesthan what is shown, with such modules residing at one or more processingdevices, which may be geographically dispersed. The browser 206 providesfor the display of a web page 216 to a user of the client device bysending and receiving data across a computer network. The web page 216may be received in response to a network request, such as a HypertextTransfer Protocol (HTTP) GET request. The web page 216 may be providedin a markup language, such as Hypertext Markup Language (HTML). The webpage 216 may also include various scripts, data, forms, and the like,including interactive and executable content such as ADOBE FLASHcontent, JAVASCRIPT content, and the like.

The browser 206 may further comprise a prerendered web page 218. Theprerendered web page 218 represents a web page that was requested andaccessed by the prerender module 210 in response to a predictednavigation event provided by the navigation prediction module 208. Inthe event the user inputs a navigation event as predicted by theprediction module 208, the browser 206 may swap the prerendered web page218 with the web page 216, thus providing the content associated withthe navigation event without the need to send another network request.In some aspects, the swap may occur before the prerendered web page 218has finished loading. In such cases, the partially loaded prerenderedweb page 218 may be swapped in to continue loading as the active page.

The memory 202 may further comprise a prerender module 210 to performfetching of a next web page as identified by the navigation predictionmodule 208. The prerender module 210 sends a network request for the webpage identified to be the likely next navigation destination that theuser will select. The web page received in response to this request isthen stored in the browser 206 as the prerendered web page 218. In someaspects, the web page request generated by the prerender module 210 isidentical to a standard web page request. In some aspects, the web pagerequest generated by the prerender module 210 comprises certain featuresto facilitate the prerender process.

The memory 202 may also store a client navigation history 212 and anaggregate navigation history 214. The client navigation history 212comprises a set of navigation events associated with past activity ofthe browser 206. The client navigation history 212 may track a set ofvisited URLs, also known as a “clickstream,” which tracks an order inwhich the user typically visits URLs (e.g. when the user visits a newswebsite, they tend to next select a URL corresponding to the top storyof the day), a set of access times associated with the URLs, and thelike. In some aspects, the client navigation history 212 comprises a setof URLs and a frequency with which the user has visited each URL. Insome aspects, the client navigation history comprises a set of URLpairs, representing a source URL and a destination URL. The aggregatenavigation history 214 may comprise similar data as the clientnavigation history 212, but keyed to multiple users rather than a singleuser. As with the client navigation history 212, the aggregatenavigation history 214 may be stored as a set of URLs and a frequencyfor each, or a set of URL pairs representing a transition from a sourceURL to a destination URL.

The client navigation history 212 and aggregate navigation history 214may represent data collected using one or more browser add-ons, scripts,or toolbars. In some aspects, the client navigation history 212 and/oraggregate navigation history 214 are maintained on a remote server, suchas the server 104, and provided to the computing device 200. Thecomputing device 200 may maintain separate records to facilitate thepredicting of a next likely navigation event, or it may act in concertwith remotely stored data. In some aspects, only aggregate navigationhistory 214 pertaining to the particular web page the user is currentlyviewing is provided to the computing device 200 (See FIGS. 6 and 8).

As described above, the aggregate navigation history data 214 can bemaintained in an anonymous fashion, with privacy protections for theindividual client data that comprises the aggregate navigation history,including, for example, anonymization of personally identifiableinformation, aggregation of data, filtering of sensitive information,encryption, hashing or filtering of sensitive information to removepersonal attributes, time limitations on storage of information, and/orlimitations on data use or sharing. The aggregate navigation history 214data can be anonymized and aggregated such that individual client datais not revealed.

FIG. 3 is a flow diagram depicting an example method 300 forprerendering a web page based upon a predicted navigation event inaccordance with aspects of the disclosure. Aspects of the method 300operate to identify one or more likely navigation destinations from aset of navigation indicators, and then prerender the identifiednavigation destinations. The method 300 may be performed by a computingdevice, such as the computing device 200, to eliminate delays in theuser web browsing experience by prerendering web pages that areidentified as likely navigation targets by the user. For example, themethod 300 may be performed by elements of the browser 206, thenavigation prediction module 208, and the prerender module 210 actingtogether. While aspects of the method 300 are described with respect tothe computing device 200, the method 300 may also be performed by theserver 104, or any device with hardware and/or software designed toaccept instructions.

At stage 302, the computing device 200 receives one or more indicatorsof navigational intent. Navigational intent may be any action that wouldtend to indicate that the user will generate a particular networkrequest, such as a request for a particular web page. For example, theindicators may provide metrics by which to determine what the particularrequest will be, such as a confidence value. For example, the user maynavigate to a certain web page, from which they generally navigate toanother certain web page based upon their browsing history, or the usermay move his mouse cursor towards a particular hyperlink embedded withina web page. In some aspects, the indicator is received from a remoteserver, such as a search engine that embeds an indicator within searchresults, indicating that most users that submit a particular searchquery select a particular search result.

At stage 304, after receiving the indicator of navigational intent, thecomputing device 200 attempts to predict the most likely navigationevent. In short, the computing device 200 makes a best guess of to wherethe user is likely to navigate next, based upon the indicator. Methodsof performing this prediction are described below. (See FIGS. 4-10).

At stage 306, the computing device 200 prerenders the content from thepredicted next navigation event as determined at stage 304. Theprerendering process may include storing a prerendered web page within abrowser, such as the prerendered web page 218. The computing device 200may prerender a single web page predicted as the most likely navigationevent, or the computing device 200 may prerender multiple pages. In someaspects, the computing device 200 determines the number of pages toprerender based upon one or more system capabilities of the computingdevice 200, such as available system resources, available networkbandwidth, processor speed, installed memory, and the like. In someaspects, the number of pages to prerender may be configurable in one ormore user settings. After prerendering the content associated with thenavigation event(s), the method 300 ends.

Multiple methods for predicting a next navigation event are providedbelow. While each method is described separately, it should beappreciated that aspects of the methods may be combined to improvenavigation prediction operations.

FIG. 4 is a flow diagram depicting an example method 400 for predictinga navigation event based on a client navigation history in accordancewith aspects of the disclosure. The method 400 provides for storing anavigation history for a user, and predicting a next navigation eventbased upon a navigation history of a particular user. As above, themethod 400 may be performed by a computing device such as the computingdevice 200. In particular, the method 400 may be performed by anavigation prediction module executing on a processor, such as thenavigation prediction module 208.

At stage 402, the computing device 200 tracks a user navigation history.For example, the computing device 200 may store records of web pagesvisited by the user, such as the browsing history commonly maintained inweb browsers. The browsing history may comprise the URLs of the webpages visited by the user, the order in which the URLs were visited, andthe manner in which the user selected the URL (e.g. whether the URL wasa clicked hyperlink, typed into an address bar, a redirect operationfrom another web page, etc.).

At stage 404, the computing device 200 determines a most likelynavigation event or events based upon the user navigation history. Themost likely navigation events may be determined by identifying theglobally most visited pages for the user, or the navigation events maybe associated with one or more current criteria. For example, thecomputing device 200 may examine the user's navigation history todetermine that, when the user is viewing a particular news web page,they almost always select a link to the top news story on that page, orthat when the user first opens the browser in the morning, they arelikely to navigate to their bank account page to check their dailybalance. The computing device 200 may employ various rules, heuristics,and filters to determine the most likely navigation event from the userhistory. The computing device 200 may associate each navigation eventwith a particular confidence value, indicating the likelihood that theuser will select each navigation event. These confidence values may thenbe used to sort the navigation events to determine the most likelynavigation event. A method to determine a confidence value for a givennavigation event is described further below (See FIG. 5).

At stage 406, the computing device 200 reports the most likelynavigation event as the predicted navigation event. For example, thesepredicted most likely navigation event may then be employed by themethod described above (See FIG. 3) to facilitate prerendering of theweb pages associated with the most likely navigation event.

FIG. 5 is a flow diagram depicting an example method 500 for computing aconfidence value for a URL using a user navigation history in accordancewith aspects of the disclosure. The method 500 is operable to tracknavigation events input by the user and to maintain a frequency valuefor each stored event. The method 500 may be employed to build a clientnavigation history as used by the method 400, and stored on thecomputing device 200 as the client navigation history 212.

At stage 502, the computing device 200 tracks the selection of aparticular URL. For example, the user may type a URL for a news siteinto the browser, or click a link on a page. The computing device 200may monitor the navigation events using functionality built into thebrowser 206, through a browser extension such as a plug-in or toolbar,or via a third party application executing in tandem with the browser.

At stage 504, the computing device 200 increments a frequency valueassociated with the URL selected at stage 502. For example, thecomputing device 200 may track a frequency value associated with eachURL selected by a user. The frequency value is a data metric used torank a number of visits to a particular web site or the number of timesa particular navigation event is selected. In response to a selectionoperation, the computing device 200 may increment the frequency valueassociated with the URL, for example by 1.0, 5.0, 10.0, 0.5, or anyother value. The frequency value associated with the URL represents howoften the user has selected the particular URL, and thus is an indicatorof how likely the user is to select the URL in the future.

At stage 506, the computing device 200 time decays the stored frequencyvalues for the URLs after a given “sweep interval”. Decaying the URLfrequency values in this manner allows for current browsing habits to bemore heavily weighted than previous browsing habits. As an example, thecomputing device 200 may execute the sweep every 30 seconds, everyminute, or every 5 minutes during which the user has selected at leastone URL. The sweep interval may be conducted in response to theselection of at least one URL during a particular sweep interval toensure that the navigation history values are not decayed below athreshold value during periods where the user is inactive. The sweep maydecay the stored frequency value associated with the URL by a particularvalue, such as 0.99, 0.5, or 1.0, or by a percentage value, such as 5%,10%, or 50%. Once the value associated with the URL drops below a giventhreshold, for example, 0.3, 1.0, or 5.0, the URL may be removed fromthe list of possible navigation destinations to avoid the list growingtoo large. After conducting the decay process, the frequency values forthe URLs may be persisted to a local storage on the computing device200, or sent to a remote storage such as provided by the server 104.

At stage 508, the stored frequency values may be used to determine therelative frequency with which the user visits particular web sites. Thefrequency value thus provides a basis from which a confidence valueassociated with a navigation event leading to each web site may bederived. In some aspects, the frequency value itself may be provided asthe confidence value. In some aspects, the confidence value isdetermined by comparing a frequency value for a particular web page withthe entire user navigation history. For example, the navigation eventwith the higher frequency value may be associated with a particularpercentage confidence value, the second highest frequency value a lowerpercentage, and the like. In some aspects, the confidence value isdetermined by frequency value by the total number of logged navigationevents. For example, the frequency value of a particular URL may bedivided by the sum of all frequency values to determine a confidencevalue.

For example, a user may be in the process of buying a home, and thusregularly checking financial and banking websites for mortgage rates.During this time, these financial and banking sites would have highvalues and thus be more likely to be prerendered, thus improving theuser experience while searching for a mortgage rate. After completingthe home purchase process, the user is likely to lose interest in day today rate fluctuations, and thus it is no longer optimal to prerenderthese websites, since the user is unlikely to visit them. As such,providing for a time decay value allows these sites to fall off of thelist over time.

FIG. 6 is a flow diagram depicting an example method 600 for predictinga navigation event based on an aggregate navigation history inaccordance with aspects of the disclosure. The method 600 is operable totrack navigation events voluntarily submitted by users to determinelikely navigation patterns. The navigation patterns are then analyzed,such as by a server 104, and supplied to the user to facilitatenavigation event prediction during the browsing process. For example, aserver, such as the server 104, may send updates to a computing device,such as the computing device 200, as the user browses to differentpages, to provide information on which link displayed on a given page ismost likely to be selected based on the aggregate navigation history.

At stage 602, the server 104 receives a set of navigation informationcomprising a browsing history. The browsing history is preferablyprovided by using an “opt-in/out” method, where the user specificallyenables (or disables) reporting functionality to provide elements oftheir browsing history to the server 104. In addition, personallyidentifying data can be anonymized and aggregated before it is stored orused, such that no personal information is stored or accessible. Abrowsing history may be tracked and provided to the server 104 via abrowser plug-in or toolbar installed on the user's computing devicewhich tracks the user's browsing history, or by the web browser itself.The browsing history may be combined with other received browsinghistories to create a set of aggregate data used in a similar manner asthe client navigation history described with respect to FIG. 4, topredict a likely navigation event. The received navigation history maybe anonymized to remove any personally identifying information. In someaspects, the received navigation history is received with individualURLs and/or transitional URL pairs provided in a hashed data format toremove any personally identifying information prior to transmission tothe server 104.

At stage 604, the server 104 determines a confidence value for each URLon a particular web page, based on the navigation information receivedat stage 602. For example, the server may employ a method similar tothat disclosed above with respect to FIG. 5 for generating confidencevalues for URLs on a page, except the navigation events are determinedbased upon aggregated data instead of specific user data. As above, theserver 104 may compute confidence values based upon the frequency valuesderived from the navigation information. In some aspects, confidencevalues are determined by the percentage of the time that users selecteda particular navigation event when they were presented with the choiceto select the particular navigation event. The transitional URL pairsprovide for the determination of a confidence value by dividing afrequency value of a source/destination URL pair by a total number ofappearances of the source URL. In some aspects, the server may determinenavigation events based upon transitions from a first page to a secondpage, rather than from a pure visit frequency metric. The server 104 maymaintain an index of web pages and associated URLs and confidence valuesfor each link on the web page, such as in a database. For example, anews site may have five URLs pointing to different news stories. Theserver 104 may receive aggregate data indicating that one of the fivenews stories is selected 60% of the time, with the other four beingselected 10% of the time each. As such, the server 104 would index thepage in a database with a 60% likelihood for the first story link, and10% likelihoods for each of the other four story links.

In some aspects, the server 104 maintains history data in a confidentialmanner, such as by converting each URL to a hash value at stage 606. Inthis manner, the server 104 may provide predicted URL data to a clientdevice without disclosing any personal user data. For example, a usermay visit a banking web page that has a particular user name andpassword login. Depending upon the user, the banking web page mayprovide URLs to each account the user possesses. Each user accessing thepage may have a different set of links provided, depending upon theaccounts the user has with the bank. By converting the links on the pageto non-reversible hash values, the server 104 may provide confidencevalues that are not associable to links on the page unless the user alsopossesses access to the same links (e.g., the client can apply the hashfunction to links they already possess on the currently viewed page todetermine if the confidence values apply). As described above, in someaspects, the hash value is computed by the computing device 200 prior tosending navigation history data to the server 104. In this manner, theserver 104 may receive the navigation history data in the hashed format,without the need to compute a hash value.

At stage 608, the server 104 transmits the hash values and confidencevalues associated with the hash values to a client device, such as thecomputing device 200. The transmittal may be in response to a requestfrom the computing device 200 for a particular URL. In some aspects, theserver 104 may transmit the hash values and confidence values inresponse to a request for such values from a service executing on theclient device 200. For example, when the computing device 200 requeststhe news web page described above, the server 104 provides the hashvalues and confidence values for the five story links present on thatpage. The computing device 200 may also request data for particular linkhash values by first generating a hash value on the client side, thenrequesting a confidence value for the particular hash value from theserver 104.

FIG. 7 is a flow diagram depicting an example method 700 for computing aconfidence value for navigation events associated with a URL using anaggregate navigation history in accordance with aspects of thedisclosure. The method 700 serves to compare navigation events from agiven URL received from a plurality of users, in order to determine howlikely each individual navigation event is. The confidence values may bedetermined in relation to a particular “source” web page, with differentconfidence values for each URL depending upon the page the user iscurrently viewing. For example, the confidence values may be used aboveas described with respect to stage 604 of the method 600 (see FIG. 6).

At stage 702, the server 104 examines received browsing histories andcomputes a number of instances for each navigation event as associatedwith a particular URL. As described above, the instance value may be apercentage or a raw number.

At stage 704, the server 104 may determine if the number of visits tothe URL exceeds a minimum threshold of statistical significance. Forexample, five visits to a particular URL may not provide statisticallysignificant data sufficient to reasonably predict a likely navigationevent away from the URL. For example, if the number of instances of theevent is less than 1000, the server 104 may proceed to stage 710, andnot calculate a probability for the event because the sample size isinsufficient.

At stage 706, the server 104 may determine if a minimum number of usershave submitted data regarding the URL to provide statisticallysignificant data. For example, the method 700 may require that at least50 users have provided data in order to compute and store a confidencevalue for the navigation event. Otherwise the method 700 may proceed tostage 710 and disregard the event until a sufficient number of usershave provided data. As above, the threshold value may fluctuatedepending upon the size of the dataset.

At stage 708, the server 104 determines a window size of recentinstances. The window size refers to the number of latest visits to theURL that will be examined to determine the confidence value, or a lengthof time to search back through the instances. The window size may bedetermined based on the amount of traffic the URL receives, how oftenthe content of the URL changes. For example, a news website that hasconstantly changing content might require a small instance window,because links from the regularly changing URL would grow stale. Awebsite with a small amount of traffic would typically require a longerwindow size in order to gather enough results for statisticalsignificance. The window size might be set at 50 instances, 100instances, 1000 instances, all instances within the last hour, withinthe last day, within the last week, or the like.

At stage 712, the server 104 computes the number of times eachparticular navigation event, such as the next URL visited for thecurrent URL, occurs within the instances defined by the window sizedetermined at stage 710. For example, out of 1000 visits to a newswebsite, a particular article might be selected 600 times, resulting ina confidence value of 60% for navigating to that article from the URL.While the present example primarily relates to determination of anavigation event based upon a number of accesses as a percentage oftotal navigation events, additional heuristics may also be used toderive the likely event based upon information supplied by the user,such as the previous navigation event (e.g. the website that led to thecurrently analyzed URL), the time of day (e.g. users are more likely tocheck news sites when in the morning when they arrive at work), theuser's location (e.g. users in a particular geographic region are likelyto check sports scores for local teams), or other demographicinformation.

At stage 714, the server 104 optionally compares the confidence valuesfor the navigations events from the URL with a threshold value. If theconfidence values do not meet the threshold value, the server 104 mayidentify a subset of available navigation events, as possible predictedlikely navigation events. In this manner the server 104 avoidspredicting navigation events when the event does not have astatistically significant likelihood of occurring, thus potentiallysaving bandwidth on prerender operations on pages that are unlikely tobe visited. The threshold may be set at a variety of different values,such as 5%, 25%, 50%, or 75%. In some aspects, the threshold may bedynamically altered based upon the number of navigation links present atthe URL, the type of URL, the traffic of the URL, the speed at whichcontent changes at the URL, and the like. If the confidence values donot meet the minimum threshold, the server 104 may filter out thepossible events that do not meet the minimum threshold.

If the navigation event or events meet the minimum threshold, or themethod 700 does not check for a minimum threshold, the most likelynavigation event or events and the likelihood for each event are storedalong with the URL at stage 716. The navigation events and confidencevalues may be supplied in response to a request to the user, such asoccurs at stage 608 described with respect to FIG. 6. The method 700ends after computing and storing the confidence values for thenavigation events associated with the URL.

FIG. 8A is a flow diagram depicting an example method 800 for predictinga navigation event based on an aggregate navigation history using hashvalues to anonymously manage link data in accordance with aspects of thedisclosure. The method 800 provides logic by which a computing device200 may predict a navigation event based upon data received from aserver 104, such as the data generated by the method 700 described withrespect to FIG. 7.

At stage 802, the computing device 200 receives a set of data from aremote server 104, the set of data comprising information associatedwith an aggregate browsing history of a web page. This aggregate datamay be received in response to a request made by the computing device200 in response to navigating to a particular web page. The aggregatedata may represent a collection of data received by a remote server froma plurality of users. For example, a web browser plug-in may allow theuser to “opt-in/out” of functionality that may send their anonymizednavigation history to a remote server. The remote server may thencollect navigation histories from a plurality of users, stored as anaggregate navigation history, such as described above (see FIG. 7). Forexample, the navigation prediction module 208 may generate a request tothe server 104 every time the user navigates to a web page, for theaggregate browsing data associated with that web page. The navigationprediction module 208 may then predict a likely next navigation eventusing the received data, so as to supply the prerender module with anext page to prerender to improve the browsing experience.

Due to the data's aggregate nature, it can be provided as a series ofhash values to protect individual user information, as described abovewith respect to FIG. 6. As such, the computing device 200 associates thereceived hash values and confidence values with the links present on thecurrent URL. To begin this process, at stage 804, the computing devicecomputes a hash value for each link on the current page using the samehash function as used by the server 104 to anonymize the link data. Asdescribed above, in some aspects the hash value is computed on thecomputing device prior to sending navigation history data to the server.In such cases, the hash value would match the original computed valuedetermined by the computing device prior to the navigation event beingtransmitted to the server, rather than a value computed on the server.

At stage 806, the computing device 200 compares the computed hash valueswith the received hash values from the server 104. In this manner, thecomputing device 200 may match the confidence values and hash valuesreceived from the server 104 with the links available for the user toselect on the currently viewed web page. The confidence values indicatea likelihood that a particular navigation event associated with the hashvalue will be selected. The computing device 200 may thus map thecurrently viewable links with the received confidence values.

At stage 808, the computing device 200 identifies the link or links withthe highest confidence value or values as the predicted next navigationevent. The method 800 ends after predicting the next navigation event.

FIG. 8B is an illustration of an example interface 810 of a web browseremploying an example method for predicting a user navigation event basedon a navigation history in accordance with aspects of the disclosure.The illustration depicts a web browser interface 810 displaying a website and a set of navigation history data 812. The web page 810comprises one or more links 814, 816, 818, 820. These links 814, 816,818, 820 may be URLs that, when selected by a user, direct the webbrowser to display a set of content associated with the selected link.

The navigation history data 812 comprises data associated with the links814, 816, 818, and two other links, Link E and Link F that are notpresent for the current user viewing the page. The navigation historydata 812 may represent an analysis of the individual user's navigationhistory (See FIGS. 4-5), or an aggregate navigation history (See FIGS.6-8). The navigation history 812 comprises information about the links814, 816, 818, and a confidence value associated with each link.

The navigation history 812 may be used by other aspects of a computingdevice 200, such as the navigation prediction module 208, to predict thenext navigation event. For example, in the present illustration,according to the navigation history 812, there is a 30% chance the userwill select Link A 814, a 60% chance the user will select Link B 816,and a 5% chance the user will select Link C 818. Link D 820 does nothave any associated data stored in the navigation history 812. The lackof data for Link D 820 may be explained in a variety of manners, such asthat the chance of selection of Link D 820 is below a threshold value,or that no data has been submitted for Link D 820. The navigationhistory 812 also displays a non-zero chance of selecting two links thatare not present, Link E and Link F. These links may have been removedfrom the web page in an update, or they may not be visible to all users,such as the user currently accessing the page. In accordance withaspects of the disclosure, the navigation prediction module 208identifies Link B 814 as a predicted next navigation event because theconfidence value of Link B 814 is greater than the values for Link A 812and Link C 818.

FIG. 9A is a flow diagram depicting an example method 900 for predictinga navigation event based on data entered within a text field inaccordance with aspects of the disclosure. The method 900, when executedby a client device such as the computing device 200, is operable topredict a next navigation event based upon text entry. The client device200 may then predict the next likely navigation event by comparing theentered text with a set of historical navigation data.

At stage 902, the computing device 200 monitors entry within a textfield, such as a URL navigation bar in a web browser, or a query entryfield on a search engine page. The text entry may comprise adestination, such as a typed URL, a search query to be submitted to asearch engine, or other data entered within a web form.

At stage 904, the computing device 200 predicts a navigation event basedupon the entered text. For example, the computing device 200 may comparethe entered text with a user browsing history. As the user enters text,the computing device 200 may search the user browsing history to comparethe partially entered text with previously visited web pages. Forexample, the user may enter “www.goo” and the computing device 200 maypredict that the user has begun typing “www.google.com” based upon theuser's past visits to www.google.com. In another aspect, the user mayenter a partial query into a search engine query entry field. As theuser enters the query, the browser may begin prerendering a results pagefor the most likely queries associated with the text that the user hasentered.

At stage 906, the navigation events as identified at stage 906 areprovided as the predicted navigation events to be used elsewhere, suchas provided by the navigation prediction module 208 and used by theprerender module 210 to prerender the predicted content.

FIG. 9B is an illustration of an example web browser interface 908employing an example method for predicting a user navigation event basedon data entered within a text field in accordance with aspects of thedisclosure. The illustration depicts a web browser interface 908 and aset of links associated with confidence values 910. For example, a webbrowser associated with the web browser interface 908 may execute on thecomputing device 200 as described above. The web browser interface 908comprises a text entry field 912. In some aspects, the text entry field912 is an address bar for entering a URL. In some aspects, the textentry field 912 may be part of a web page, such as a search string entryfield on a search engine web site.

As the user enters text within the text entry field 912, the computingdevice 200 determines a most likely destination from the entered text.For example, the user may enter the word “Pizza” in the text entry field912. Based upon the user's intent to navigate to a page related toPizza, the system determines there is an 80% chance the user wishes tonavigate to “joespizza.com”, and a 10% chance to navigate to each of“davespizza.com” or “stevespizza.com”. The percentage values for eachlink may be determined by a variety of data analysis methods, takinginto account factors such as the user's navigation history, an aggregatenavigation history, sponsorship from various result web pages, and thelike. In the previous example, “joespizza.com” would be identified asthe predicted navigation event based upon the fact that it is associatedwith the highest percentage.

FIG. 10A is a flow diagram depicting an example method 1000 forpredicting a navigation event based on mouse cursor movement inaccordance with aspects of the disclosure. The method 1000 allows acomputing device, such as the computing device 200, to predict anavigation event by the position of a mouse cursor on a screen. Similarconcepts might also be applicable to other forms of user input, such asTRACK-IR, keyboard inputs, light pens, trackballs, or any other inputdevice capable of manipulating a cursor.

At stage 1002, the computing device 200 monitors cursor movement. Forexample, the computing device 200 may monitor a mouse cursor as a userbrowses and scrolls through a web page. The computing device 200 maytrack the location, speed, and acceleration of the cursor on the page.

At stage 1004, the computing device 200 predicts a likely navigationevent from the cursor movement as monitored at stage 1002. Thenavigation event may be predicted by identifying the link closest to thecursor, by extrapolating the cursor position based on the cursor'sdirection of movement, by determining a speed and direction and mostlikely destination of the cursor, by identifying a link under thecursor, or the like. Aspects of the disclosure may select multiple linksor navigation events for prediction in the event the extrapolatedposition of the cursor passes through multiple links. The computingdevice 200 may also select multiple links within a certain distance ofthe cursor, and in some aspects may decrease a value for links fromwhich the cursor is moving away. After predicting one or more nextnavigation events based on cursor movement, the method 1000 ends.

FIG. 10B is an illustration of an example web browser interface 1008 ofemploying an example method for predicting a user navigation event basedon a mouse cursor movement in accordance with aspects of the disclosure.The illustration comprises a web browser interface 1008 displaying a webpage comprising three links 1010, 1012, 1014 and a cursor. The cursorhas an initial position 1016, and then moves to a second position 1018.The two positions 1016 and 1018 are used to determine a line 1020indicating the likely future position of the cursor. The likely nextnavigation event is predicted by extending the line 1020 to determinewhich of the three links is intersected, or nearly intersected. In thepresent example, the line 1020 intersects with Link C 1014, or comesvery close to it. As such, Link C 1014 is identified as a predictednavigation event.

The stages of the illustrated methods described above are not intendedto be limiting. The functionality of the methods may exist in a fewer orgreater number of stages than what is shown and, even with the depictedmethods, the particular order of events may be different from what isshown in the figures.

The systems and methods described above advantageously provide for animproved browsing experience. By predicting the next navigation event,the browser can perform prerender operations to minimize the amount oftime users wait for web pages to load. Multiple methods to perform theprerender operations provide a flexible and robust system fordetermining the next navigation event.

As these and other variations and combinations of the features discussedabove can be utilized without departing from the disclosure as definedby the claims, the foregoing description of the embodiments should betaken by way of illustration rather than by way of limitation of thedisclosure as defined by the claims. It will also be understood that theprovision of examples of the disclosure (as well as clauses phrased as“such as,” “e.g.”, “including” and the like) should not be interpretedas limiting the disclosure to the specific examples; rather, theexamples are intended to illustrate only some of many possibleembodiments.

1. A computer-implemented method for predicting a navigation event, themethod comprising: receiving an indicator of navigational intent,wherein the indicator is at least one of a browsing history, a textentry, or a cursor input; predicting, using a processor, a nextnavigation event from the indicator, wherein the next navigation eventis a uniform resource locator; and prerendering content associated withthe next navigation event.
 2. A computer-implemented method forpredicting a navigation event, the method comprising: tracking anavigation history; calculating one or more confidence values for one ormore of a plurality of navigation events using the navigation history;determining, using a processor, one or more likely navigation eventsusing the confidence values; and identifying at least one of the one ormore likely navigation events as a predicted navigation event.
 3. Themethod of claim 2, further comprising retrieving content associated withthe predicted navigation event.
 4. The method of claim 2, whereincalculating the one or more confidence values comprises: monitoring forthe selection of a first uniform resource locator; incrementing, inresponse to the selection, a frequency value associated with the firstuniform resource locator or a frequency value associated with a pair ofuniform resource locators, the pair comprising the first uniformresource locator and a source uniform resource locator; storing thefrequency value in a memory; and determining a confidence value for theuniform resource locator or pair of uniform resource locators from atleast one frequency value stored in the memory.
 5. The method of claim4, further comprising decaying a frequency value for non-selecteduniform resource locators after a predetermined time interval.
 6. Themethod of claim 5, wherein the decaying of the frequency value for thenon-selected uniform resource locators is performed in response to theselection of the first uniform resource locator.
 7. The method of claim2, wherein the navigation history is associated with at least one of aparticular client or a particular user.
 8. The method of claim 2,wherein the navigation history is associated with a plurality of users.9. The method of claim 8, further comprising: computing a first hashvalue for a navigation event associated with a first uniform resourcelocator or a transitional pair of uniform resource locators, wherein thetransitional pair comprises a source uniform resource locator and adestination uniform resource locator; computing a confidence value forthe navigation event; and transmitting the hash value and the confidencevalue, such that a receiver of the first hash value and the confidencevalue computes a second hash value of a second uniform resource locatorto identify the first uniform resource locator to which the confidencevalue applies.
 10. The method of claim 8, wherein determining the mostlikely navigation event comprises: for at least one uniform resourcelocator (URL), computing a most visited subsequent URL based on thenavigation history of the plurality of users.
 11. The method of claim10, further comprising determining if the number of visits to thesubsequent uniform resource locator is greater than a threshold numberof visits.
 12. The method of claim 10, further comprising determining ifa number of users submitting data for the subsequent uniform resourcelocator is greater than a threshold number of users.
 13. The method ofclaim 10, further comprising: identifying a window of recent visits tobe analyzed to determine the most visited subsequent URL; and analyzingvisits within the identified window.
 14. The method of claim 13, whereinthe window is specified by a time period or a number of visits.
 15. Themethod of claim 2, wherein the navigation history comprises at least oneof a uniform resource locator or a transitional pair of uniform resourcelocators, wherein the transitional pair of uniform resource locatorscomprises a source uniform resource locator and a destination uniformresource locator.
 16. A computer-implemented method of predicting a nextnavigation event, the method comprising: receiving a set of data for auniform resource locator, the set of data comprising hash valuesassociated with one or more links associated with the uniform resourcelocator and a set of confidence values associated with the one or morelinks; computing, using a processor, a hash value for one or more linkspresent on a page associated with the uniform resource locator;comparing the computed hash values with the received hash values to mapeach computed hash value to a received hash value; and identifying aconfidence value associated with each visible link based upon thereceived confidence value associated with the received hash value towhich the computed hash value for the link maps.
 17. The method of claim16, further comprising predicting one or more next navigation events,where the one or more predicted next navigation events relate to a linkwith the highest identified confidence value.
 18. A method forpredicting a next navigation event, the method comprising: monitoringtext entry within a text entry field; predicting, using a processor, alikely uniform resource locator or likely query based upon the textentry; and identifying the likely uniform resource locator or likelyquery as a predicted next navigation event.
 19. The method of claim 18,wherein predicting the likely URL comprises comparing the text entrywith a user history to identify a previously visited uniform resourcelocator.
 20. The method of claim 18, wherein predicting the likely querycomprises comparing the text entry with a set of previously enteredsearch queries to identify a likely next query as the next navigationevent.
 21. The method of claim 18, further comprising identifying a setof search results associated with the identified likely next query. 22.The method of claim 21, further comprising identifying a most relevantsearch result from the set of search results as the predicted nextnavigation event.
 23. The method of claim 18, wherein predicting thelikely query comprises receiving a set of possible queries from a searchengine based upon the text entry.
 24. A computer-implemented method forpredicting a next navigation event, the method comprising: monitoringmovement of a cursor within a browser, the browser displaying a web pagewith one or more hyperlinks; and predicting, using a processor, a nextnavigation event by identifying at least one of a hyperlink toward whichthe cursor is moving or a hyperlink on which the cursor is located. 25.The method of claim 24, further comprising prerendering a web pageassociated with the identified hyperlink.
 26. The method of claim 24,further comprising extrapolating the movement of the cursor to identifya line, and identifying one or more of the hyperlinks on the identifiedline as the next navigation event.
 27. The method of claim 24, furthercomprising calculating a speed of the cursor and a distance to each ofthe hyperlinks to determine to which of the hyperlinks the cursor islikely to be traveling.
 28. A processing system for predicting a nextnavigation event comprising: at least one processor; a navigationprediction module associated with the at least one processor; and memoryfor storing navigation data, the memory coupled to the at least oneprocessor; wherein the navigation prediction module is configured tocalculate one or more confidence values for one or more of a pluralityof navigation events using the navigation data, to determine one or morelikely navigation events using the confidence values, and to identify atleast one of the one or more likely navigation events as a predictednavigation event.
 29. The system of claim 28, wherein the navigationdata comprises at least one of a browsing history, a text entry, or acursor input.